Improving Public Transport Planning Using Smart Card and MaaS Subscription Data
Lead Research Organisation:
Lancaster University
Department Name: Management Science
Abstract
In this proposal, I want to develop optimisation frameworks that will use smart card and mobility-as-a-service subscription data to address strategic (transit route network design problem) and (if time allows) tactical (frequency setting and timetabling) public transport planning problems.
The use of public transportation is decreasing all around the UK, except London, every year. Last year, more than 80% of the distances are travelled by private cars in Britain. There may be many reasons for the lack of enthusiasm for public transport, e.g. poor connections, infrequent service or high cost. Making public transport again the main mode of transport is important for many reasons. First, the UK's zero carbon emission by 2050 pledge requires to use more energy-efficient modes. More than 16% of the emissions in the UK are from private cars. Second, cars waste road space, they need almost eight times more space per passenger compared to busses. Excessive private car use is one of the biggest reasons for traffic congestion we experience every day. We need to find ways to move mode choice from private to public transport.
One way of altering user behaviour from private to public transport could be frameworking mobility as a service (MaaS) and provide packages that could provide different options to users. MaaS is not a new concept and is in use of different forms all around the world. However, these systems either are just a design concept a platform that provides multiple options and unified payment method for any trip to its users or only allows subscription packages that allow users to access mass public transport modes. In this project, in addition to smart card data, I will be able to work with a subscription-based MaaS data that provides mass and personal public transport (e.g. bikesharing, carsharing, ride-hailing, shared ride-hailing, dial-a-ride) to its users together.
Intelligent transportation systems (ITP) applications help us monitor and control public transport system. We also use the collected ITP data to analyse and improve those systems. However, the existing classical models are usually not capable of utilising the mass data produced by the ITP systems every day. We need to develop better methods to deal with high precision data.
In this proposal, I aim to develop models that will improve public transport planning by using smart card and MaaS subscription data. In my approach, in addition to mass public transit lines, I want to consider personal public transport options in the development of public transport network design and (if time allows) frequency setting and timetabling problems.
With the advancement in mobile technologies and trend towards sharing economy, we see more personal public transport modes in cities. They are encouraged by especially the big cities to provide alternative modes of transportation to their dwellers. We see more people use these transport modes every day. Considering all types of public transportation modes in designing transit route networks could provide better public transport plans for limited resources and eventually increase average welfare for the urban dwellers living in these cities.
The use of public transportation is decreasing all around the UK, except London, every year. Last year, more than 80% of the distances are travelled by private cars in Britain. There may be many reasons for the lack of enthusiasm for public transport, e.g. poor connections, infrequent service or high cost. Making public transport again the main mode of transport is important for many reasons. First, the UK's zero carbon emission by 2050 pledge requires to use more energy-efficient modes. More than 16% of the emissions in the UK are from private cars. Second, cars waste road space, they need almost eight times more space per passenger compared to busses. Excessive private car use is one of the biggest reasons for traffic congestion we experience every day. We need to find ways to move mode choice from private to public transport.
One way of altering user behaviour from private to public transport could be frameworking mobility as a service (MaaS) and provide packages that could provide different options to users. MaaS is not a new concept and is in use of different forms all around the world. However, these systems either are just a design concept a platform that provides multiple options and unified payment method for any trip to its users or only allows subscription packages that allow users to access mass public transport modes. In this project, in addition to smart card data, I will be able to work with a subscription-based MaaS data that provides mass and personal public transport (e.g. bikesharing, carsharing, ride-hailing, shared ride-hailing, dial-a-ride) to its users together.
Intelligent transportation systems (ITP) applications help us monitor and control public transport system. We also use the collected ITP data to analyse and improve those systems. However, the existing classical models are usually not capable of utilising the mass data produced by the ITP systems every day. We need to develop better methods to deal with high precision data.
In this proposal, I aim to develop models that will improve public transport planning by using smart card and MaaS subscription data. In my approach, in addition to mass public transit lines, I want to consider personal public transport options in the development of public transport network design and (if time allows) frequency setting and timetabling problems.
With the advancement in mobile technologies and trend towards sharing economy, we see more personal public transport modes in cities. They are encouraged by especially the big cities to provide alternative modes of transportation to their dwellers. We see more people use these transport modes every day. Considering all types of public transportation modes in designing transit route networks could provide better public transport plans for limited resources and eventually increase average welfare for the urban dwellers living in these cities.
